Abarbanel, H. D. I. (1996). Analysis of Observed Chaotic Data, Retrieved from www.scopus.com
Abd Hamid, N. Z. (2018). Application of chaotic approach in forecasting highland's temperature time series. Paper presented at the IOP Conference Series: Earth and Environmental Science, , 169(1) doi:10.1088/1755-1315/169/1/012107 Retrieved from www.scopus.com
Adenan, N. H., & Noorani, M. S. M. (2016). Multiple time-scales nonlinear prediction of river flow using chaos approach. Jurnal Teknologi, 78(7), 1-7. doi:10.11113/jt.v78.3561
Albostan, A., & Önöz, B. (2015). Implementation of chaotic analysis on river discharge time series. Energy Power Eng, 7(3), 81-92. Retrieved from www.scopus.com
Cheng, A., Jiang, X., Li, Y., Zhang, C., & Zhu, H. (2017). Multiple sources and multiple measures based traffic flow prediction using the chaos theory and support vector regression method. Physica A: Statistical Mechanics and its Applications, 466, 422-434. doi:10.1016/j.physa.2016.09.041
Fragkou, A. D., Karakasidis, T. E., & Nathanail, E. (2018). Detection of traffic incidents using nonlinear time series analysis. Chaos, 28(6) doi:10.1063/1.5024924
Frazier, C., & Kockelman, K. M. (2004). Chaos theory and transportation systems: Instructive example doi:10.3141/1897-02 Retrieved from www.scopus.com
Hamid, N. Z. A., Adenan, N. H., & Noorani, M. S. M. (2017). Forecasting and analyzing high O3 time series in educational area through an improved chaotic approach. Paper presented at the AIP Conference Proceedings, , 1870 doi:10.1063/1.4995867 Retrieved from www.scopus.com
Hilborn, R. C. (1994). Chaos and Nonlinear Dynamics, Retrieved from www.scopus.com
Kumar, K., Parida, M., & Katiyar, V. K. (2013). Short term traffic flow prediction for a non urban highway using artificial neural network. Procedia-Social and Behavioral Sciences, 104, 755-764. Retrieved from www.scopus.com
Lan, L. W., Lin, F. Y., & Kuo, A. Y. (2003). Testing and prediction of traffic flow dynamics with chaos. Journal of the Eastern Asia Society for Transportation Studies, 5, 1975-1990. Retrieved from www.scopus.com
Lorenz, E. N., & Hilborn, R. C. (1995). The essence of chaos. Phys.Today, 63(48), 862-863. Retrieved from www.scopus.com
Peng, D., Xin, F., Zhang, L., Gao, Z., Zhang, W., Wang, Y., . . . Wang, Y. (2017). Nonlinear time-series analysis of optical signals to identify multiphase flow behavior in microchannels. AIChE Journal, 63(6), 2378-2385. doi:10.1002/aic.15584
Priambodo, B., & Ahmad, A. (2018). Traffic flow prediction model based on neighbouring roads using neural network and multiple regression. Journal of Information and Communication Technology, 17(4), 513-535. Retrieved from www.scopus.com
Rosenstein, M. T., Collins, J. J., & De Luca, C. J. (1993). A practical method for calculating largest lyapunov exponents from small data sets. Physica D: Nonlinear Phenomena, 65(1-2), 117-134. doi:10.1016/0167-2789(93)90009-P
Safonov, L. A., Tomer, E., Strygin, V. V., Ashkenazy, Y., & Havlin, S. (2002). Delay-induced chaos with multifractal attractor in a traffic flow model. Europhysics Letters, 57(2), 151-157. doi:10.1209/epl/i2002-00555-0
Shang, P., Li, X., & Kamae, S. (2005). Chaotic analysis of traffic time series. Chaos, Solitons and Fractals, 25(1), 121-128. doi:10.1016/j.chaos.2004.09.104
Shang, P., Na, X., & Kamae, S. (2009). Chaotic analysis of time series in the sediment transport phenomenon. Chaos, Solitons and Fractals, 41(1), 368-379. doi:10.1016/j.chaos.2008.01.014
Sivakumar, B. (2002). A phase-space reconstruction approach to prediction of suspended sediment concentration in rivers. Journal of Hydrology, 258(1-4), 149-162. doi:10.1016/S0022-1694(01)00573-X
Vlad, S. (2010). Investigation of chaotic behavior in euro-leu exchange rate. Journal of Applied Computer Science and Mathematics, 8(4), 67-71. Retrieved from www.scopus.com
Vlahogianni, E. I., Golias, J. C., & Karlaftis, M. G. (2004). Short‐term traffic forecasting: Overview of objectives and methods. Transport Reviews, 24(5), 533-557. doi:10.1080/0144164042000195072
Xie, J., & Choi, Y. -. (2017). Hybrid traffic prediction scheme for intelligent transportation systems based on historical and real-time data. International Journal of Distributed Sensor Networks, 13(11) doi:10.1177/1550147717745009
Xue, J. -., & Shi, Z. -. (2008). Short-time traffic flow prediction using chaos time series theory. Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/ Journal of Transportation Systems Engineering and Information Technology, 8(5), 68-72. Retrieved from www.scopus.com
Zaim, W. N. A. B. W. M., & Hamid, N. Z. A. (2017). Forecasting ozone pollutant (o3) in universiti pendidikan sultan idris, tanjung malim, perak, malaysia based on monsoon using chaotic approach. [Peramalan Bahan Pencemar Ozon (O3) di Universiti Pendidikan Sultan Idris, Tanjung Malim, Perak, Malaysia Mengikut Monsun dengan Menggunakan Pendekatan Kalut] Sains Malaysiana, 46(12), 2523-2528. doi:10.17576/jsm-2017-4612-30